Without limiting the scope of the disclosure, its background is described in connection with oil and gas reservoirs. Current field analysis indicates that approximately 30% of multi-stage fracture clusters in tight and unconventional reservoirs are not effective, while 80% of practitioners report that they do not have an adequate understanding of reservoir drainage volume. Field data also shows that the total drilling time is consistently being reduced, which leaves the time spent in improved completions, especially multi-stage fracture design, as increasingly important for optimization.
Three-dimensional earth models play an increasingly central role in the petroleum industry. They are routinely used to plan new wells, calculate hydrocarbon reserves and forecast production. Usually due to sparse well coverage, earth models are often poorly constrained away from the well locations. This is where dynamic reservoir characterization could play an important role. Calibration of a dynamic reservoir characterization is essentially solving an inverse problem, i.e. finding the “best” model(s) under historical production data constraints, which produces (by forward simulation) the closest calculated results compared to the observed dynamic data, e.g. production rate, gas oil ratio, and wellbore pressure. If the dynamic reservoir characterization is performed successfully, it is able to constrain the models, reduce uncertainties, and provide better predictions. The forward modeling technique to be used with the dynamic reservoir characterization should be selected according to specific problems. It is very important to strike a balance between accuracy and efficiency. The model has to be “accurate” enough in order to capture the principal physics. Efficiency is a big concern because the solution of the inverse problem usually involves not just one forward simulation but many. The traditional 3D finite difference (FD) reservoir simulation may not be the best fit for all dynamic reservoir characterization applications, particularly for high resolution geo-models consisting of multimillion cells.